SoroushVahidi/scidocs huggingface dataset:
Accidental death due to complete autoerotic asphyxia associated with transvestic fetishism and anal self-stimulation - case report.
(2009)

A case is reported of a 36-year-old male, found dead in his locked room, lying on a bed, dressed in his mother's clothes, with a plastic bag over his head, hands tied and with a barrel wooden cork in his rectum. Two pornographic magazines were found on a chair near the bed, so that the deceased could see them well. Asphyxia was controlled with a complex apparatus which consisted of two elastic luggage rack straps, the first surrounding his waist, perineum, and buttocks, and the second the back of his body, and neck. According to the psychological autopsy based on a structured interview (SCID-I, SCID-II) with his father, the deceased was single, unemployed and with a part college education. He had grown up in a poor family with a reserved father and dominant mother, and was indicative of fulfilling DSM-IV diagnostic criteria for alcohol dependence, paraphilia involving hypoxyphilia with transvestic fetishism and anal masturbation and a borderline personality disorder. There was no evidence of previous psychiatric treatment. The Circumstances subscale of Beck's Suicidal Intent Scale (SIS-CS) pointed at the lack of final acts (thoughts or plans) in anticipation of death, and absence of a suicide note or overt communication of suicidal intent before death. Integration of the crime scene data with those of the forensic medicine and psychological autopsy enabled identification of the event as an accidental death, caused by neck strangulation, suffocation by a plastic bag, and vagal stimulation due to a foreign body in the rectum.
Quadruped Robot Running With a Bounding Gait
(2000)

Scout II, an autonomous four-legged robot with only one actuator per compliant leg is described. We demonstrate the need to model the actuators and the power source of the robot system carefully in order to obtain experimentally valid models for simulation and analysis. We describe a new, simple running controller that requires minimal task level feedback, yet achieves reliable and fast running up to 1.2 m/s. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. In addition, the simple mechanical design of our robot may be used as a template for the control of higher degree of freedom quadrupeds. An energetics analysis reveals a highly efficient system with a specific resistance of 0.32 when based on mechanical power dissipation and of 1.0 when based on total electrical power dissipation.
Virtual Engineering Factory: Creating Experience Base for Industry 4.0
(2016)

In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNAbased knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects (VEO), virtual engineering processes (VEP), and virtual engineering factories (VEF), respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical systembased Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.
Linear Model Selection by Cross-Validation
(2010)

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association. We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the Cp, and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of observations n-s o. We show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-n,-out cross-validation with nv, the number of observations reserved for validation, satisfying no/n-1 I as n s* xoo. This is a somewhat shocking discovery, because ne/n-* 1 is totally opposite to the popular leave-one-out recipe in cross-validation. Motivations, justifications, and discussions of some practical aspects of the use of the leave-n,-out cross-validation method are provided, and results from a simulation study are presented.
An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records
(1997)

Detecting database records that are approximate duplicates, but not exact duplicates, is an important task. Databases may contain duplicate records concerning the same real-world entity because of data entry errors, because of un-standardized abbreviations, or because of diierences in the detailed schemas of records from multiple databases, among other reasons. In this paper, we present an eecient algorithm for recognizing clusters of approximately duplicate records. Three key ideas distinguish the algorithm presented. First, a version of the Smith-Waterman algorithm for computing minimum edit-distance is used as a domain-independent method to recognize pairs of approximately duplicate records. Second, the union//nd algorithm is used to keep track of clusters of duplicate records incrementally, as pairwise duplicate relationships are discovered. Third, the algorithm uses a priority queue of cluster subsets to respond adaptively to the size and homogeneity of the clusters discovered as the database is scanned. This typically reduces by over 75% the number of times that the expensive pair-wise record matching (Smith-Waterman or other) is applied, without impairing accuracy. Comprehensive experiments on synthetic databases and on a real database of bibliographic records connrm the eeectiveness of the new algorithm.
Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment
(2000)

We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints connectivity and biconnectivity, and one optimization objective maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
(2000)

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adap tive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
Parssec: A Parallel Simulation Environment for Complex Systems
(1998)

S ystems are now being designed that can scale to very large configurations. Examples include parallel architectures with thousands of processors, wireless networks connecting tens of thousands of computers, and parallel database systems capable of processing millions of transactions a second. In some cases, systems have grown considerably larger than envisaged by their designers. Design and development costs for such systems could be significantly reduced if only there were efficient techniques for evaluating design alternatives and predicting their impact on overall system performance metrics. Due to the systems' analytical intractability, simulation is the most common performance evaluation technique for such systems. However, the long execution times needed for sequential simulation models often hamper evaluation. For instance, it can easily take weeks to simulate wireless networks with thousands of mobile nodes that are subject to the interference and signal-fading effects of a realistic environment. Also, the simulation of parallel programs typically suffers from slowdown factors of 30 to 50 per processor. 1 This means that simulation of a parallel program executing for five to 15 minutes on a 128-node multi-processor can take anywhere from a few weeks to a few months—even on state-of-the-art sequential workstations. As the size of the physical system increases, the models' memory requirements can easily exceed the workstations' capacity. The limitations of sequential model execution have led to growing interest in the use of parallel execution for simulating large-scale systems. Widespread use of parallel simulation, however, has been significantly hindered by a lack of tools for integrating parallel model execution into the overall framework of system simulation. Although a number of algorithmic alternatives exist for parallel execution of discrete-event simulation models, performance analysts not expert in parallel simulation have relatively few tools giving them flexibility to experiment with multiple algorithmic or architectural alternatives for model execution. Another drawback to widespread use of simulations is the cost of model design and maintenance. The design and development costs for detailed simulation models for complex systems can easily rival the costs for the physical systems themselves. The simulation environment we developed at UCLA attempts to address some of these issues by providing these features: • An easy path for the migration of simulation models to operational software prototypes. • Implementation on both distributed-and shared-memory platforms and support for a diverse set of parallel simulation protocols. • Support for visual and hierarchical model design. Our environment consists of three primary components: …
The volcanormal density for radar-based extended target tracking
(2017)

This paper presents a novel shape model for radar-based vehicle tracking. This model is a special probability density function (pdf), designed to approximate the radar measurement spread of a vehicle more adequately than widely used models. While this pdf is used to determine the likelihood of the spatial measurements, additionally a recent Doppler measurement model is integrated in a maximum likelihood estimator. This formulation allows the estimation of a vehicle's pose, motion and axis lengths based on the radar's spatial and Doppler dimension. On this basis, an efficient computation method is derived. An evaluation of actual radar and ground truth data demonstrates the precise estimation of a vehicle's state.
The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data
(2015)

Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing bias towards the majority class. In this study, we seek to determine if the inclusion of data sampling will improve the performance of the Random Forest classifier. In order to test the effect of data sampling, we used Random Undersampling along with two post-sampling class distribution ratios: 35:65 and 50:50 (minority:majority). Additionally, we also built inductive models with Random Forest when no data sampling technique was applied, so we can observe the true effect of the data sampling. All three options were tested on a series of fifteen imbalanced bioinformatics datasets. Our results show that data sampling does improve the classification performance of Random Forest, especially when using the 50:50 post-sampling class distribution ratio. However, statistical analysis shows that the increase in performance is not statistically significant. Thus, we can state that while data sampling does improve the classification performance of Random Forest, it is not a necessary step as the classifier is fairly robust to imbalanced data on its own.
Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis
(2018)

Accurate and early diagnosis of Alzheimer’s disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.
Smart home design based on ZigBee wireless sensor network
(2012)

This work is based on the Internet of Things and ZigBee wireless sensor network technology. A kind of smart home design based on ZigBee wireless sensor network was proposed in this paper. Texas Instruments MCU device LM3S9B96, which is the ARM Cortex-M3 based controllers, was used in this system. The entire system is running on the μC/OS-II embedded real-time multitasking operating system. Users can access this system by a dynamic webpage of LwIP TCP/IP protocol stack or GSM SMS. Using this system users can conveniently know the environment parameters of home, such as temperature, humidity, meter readings, light, and control the home electronic equipments, such as light, aircondition, heater, by ZigBee wireless sensor network.
Analytics using R for predicting credit defaulters
(2016)

Nowadays there are many risks related to bank loans, especially for the banks so as to reduce their capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behaviour related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims to extract useful knowledge from tremendous amount of complex data sets. This work aims to develop a model and construct a prototype for the same using a data set available in the UCI repository. The model is a decision tree based classification model that uses the functions available in the R Package. Prior to building the model, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The final model is used for prediction with the test dataset and the experimental results prove the efficiency of the built model.
Two Methods for Semi-automatic Image Segmentation based on Fuzzy Connectedness and Watersheds
(2001)

At the present time, one of the best methods for semiautomatic image segmentation seems to be the approach based on the fuzzy connectedness principle. First, we identify some deficiencies of this approach and propose a way to improve it, through the introduction of competitive learning. Second, we propose a different approach, based on watersheds. We show that the competitive fuzzy connectedness-based method outperforms the noncompetitive variant and generally (but not always) outperforms the watershed-based approach. The competitive variant of the fuzzy connectedness-based method can be a good alternative to the watersheds.
Generalized Semi-Orthogonal Multiple-Access for Massive MIMO
(2015)

We propose a novel framework to enable concurrent transmission of multiple users to an access node equipped with a massive multiple-input multiple-output (mMIMO), which encompasses and extends the conventional time-division duplex (TDD) protocol and the recently proposed semi-orthogonal multiple-access (SOMA). The new solution is referred to generalized semi-orthogonal multiple-access (GSOMA). To enable GSOMA, the users are grouped such that the users within each group are assigned mutually orthogonal pilot sequences. However the pilot sequences can be reused in different groups and the users in different group are coordinated such that their pilot sequences do not interfere by a semi-orthogonal mechanism enabled by a resource-offset sequence. We describe the general framework and analyze a case of GSOMA and show that a properly designed GSOMA can outperform the conventional TDD and SOMA.
A survey of top-k query processing techniques in relational database systems
(2008)

Efficient processing of top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-k processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top-k queries in XML domain, and show their connections to relational approaches.
Quaternion Domain  $k$ -Means Clustering for Improved Real Time Classification of E-Nose Data
(2016)

This paper proposes a novel clustering technique implemented in the quaternion domain for qualitative classification of E-nose data. The proposed technique is in many ways similar to the popular k-means clustering algorithm. However, computations carried out in the quaternion space have yielded better class separability and higher cluster validity. A pool of possible cluster centers was created by subjecting each initial center to a fixed rotation in the quaternion space. The test samples were then compared with each of the centers in the pool and assigned to an appropriate center using minimum Euclidean distance criterion. The evolving clusters have been evaluated periodically for their compactness and interclass separation using the Davis-Bouldin (DB) index. The set of clusters having minimum DB index was chosen as an optimal one. It was observed that using the proposed technique the inverse DB index remains significantly higher with successive iterations implying a consistent performance on the cluster validity front. Furthermore, clusters formed using quaternion algebra have been observed to have a smaller DB index. Finally, when compared with the traditional k-means algorithm, the proposed technique performed significantly better in terms of percentage classification of unlabeled samples.
On Summarizing Large-Scale Dynamic Graphs
(2017)

How can we describe a large, dynamic graph over time? Is it random? If not, what are the most apparent deviations from randomness – a dense block of actors that persists over time, or perhaps a star with many satellite nodes that appears with some fixed periodicity? In practice, these deviations indicate patterns – for example, research collaborations forming and fading away over the years. Which patterns exist in real-world dynamic graphs, and how can we find and rank their importance? These are exactly the problems we focus on. Our main contributions are (a) formulation: we show how to formalize this problem as minimizing an information theoretic encoding cost, (b) algorithm: we propose TIMECRUNCH, an effective and scalable method for finding coherent, temporal patterns in dynamic graphs and (c) practicality: we apply our method to several large, diverse real-world datasets with up to 36 million edges and introduce our auxiliary ECOVIZ framework for visualizing and interacting with dynamic graphs which have been summarized by TIMECRUNCH. We show that TIMECRUNCH is able to compress these graphs by summarizing important temporal structures and finds patterns that agree with intuition.